Prokaryotes are organisms that lack a nucleus or any membrane-bound organelles and are generally unicellular. Most prokaryotes can be broadly categorized into Gram-positive or Gram-negative, based on the peptidoglycan of Gram-positive microbes' outer cell wall staining with crystal violet and safranin (or carbol fuchsin), although some prokaryotes have a variable Gram stain. Prokaryotes contain 16S (Svedberg unit) rRNA, which is a component of the 30S small subunit of prokaryotic ribosomes. 16S rRNA is approximately 1500 nucleotides in length, encoded by the 16S rRNA gene (sometimes referred to as 16S rDNA), which is generally part of a co-transcribed operon also containing the 23S and 5S rRNA genes. The DNA sequence of the 16S rRNA genes (and thus the RNA sequence of the 16S rRNA molecules) is highly conserved between prokaryotes, although there are regions of variation (Weisburg W G, et al., (1991) J Bacterial. 173 (2): 697-703).
In contrast, eukaryotes are organisms containing membranes within cells, in particular a nuclear membrane containing DNA, and can be unicellular (e.g. most yeasts) or multicellular. All eukaryotes contain 5.8S rRNA, which is a component of the large 60S small subunit of eukaryotic ribosomes. Its length varies between species but it is usually around 160 nucleotides. The 5.8S rRNA gene (often referred to as 5.8S rDNA) is part of the 45S rDNA, which also contains the 18S and 28S rRNA genes separated by 2 internally transcribed spacers. In humans, the 45S rDNA is present in 5 clusters on 5 different chromosomes, each cluster having 30-40 repeats. The 45S rDNA is transcribed by RNA polymerase I as a single transcription unit (45S), which is then processed to produce the 5.8S, 18S and 28S rRNA molecules. The sequence of 5.8S rRNA gene (and thus the sequence of the 5.8S rRNA molecule) is highly conserved between eukaryotes, although there are regions of variation that can be used in phylogenetic studies (Field K. G et al., (1988) Science 239(4841): 748-753).
It is often desirable to classify prokaryotic and eukaryotic microbes in a sample. For example, the classification of microbes that contaminate solutions, materials or foodstuffs, and pose a threat to the wellbeing of other organisms or the quality of production of solutions or materials or foodstuffs, assists in the identification of pathogens, and management, control, eradication, elimination, limitation or removal of such microbes. It may also be desirable to determine the natural microbial population (the “microbiome”) of a sample, such as for ecological studies of microbial diversity, phylum spectrum, relative phylum abundance (Gehron, M. J. et al. (1984) J. Microbiol. Methods 2, 165-176; Claesson, M. J. et al. (2010) Nucl Acids Res 38(22), e200), or for determining or monitoring deviation of the microbiome balance from a normal state in pathological conditions, such as enteric (e.g. gastroenteritis, rumenitis, colitis, typhlitis; Bailey, S. R., et al. (2003). Appl. Environ. Microbiol. 69, 2087-2093), respiratory (e.g. pneumonia, bronchitis, mucositis), urinary (e.g. cystitis, nephritis, urethritis) and skin (e.g. wounds, pruritis, dermatitis, psoriasis) disorders including viral, fungal, parasitic and bacterial infections. It may also be useful to determine the microbiome of a sample in response to therapies or treatments or modulations such as the use of antibiotics, steroids, immune modulators, pre and probiotics, soil or water treatments, filtration, sterilization procedures, antiseptics.
Current microorganism classification schemes include, but are not limited to, phenotypic, chemotypic and genotypic. Within phenotypic classification are the sub-classification methods of Gram staining, growth requirements, biochemical reactions, antibiotic sensitivity, serological systems, environmental reservoirs (or where such microbes preferentially live and grow). Within chemotypic classification are the sub-classification methods using various technologies that can include analysis of microbial components consisting of sugars, fats, proteins or minerals. Within genotypic classification are the sub-classification methods of restriction fragment length analysis and ribosomal RNA sequence analysis, both reliant on different levels of interpretation of genetic material.
Phenotypic classification methods can suffer from a lack of sensitivity and specificity, are not rapid or easy, and have limited ability to be automated. Chemotypic methods also lack sensitivity and specificity and currently are not cost effective. Genotypic methods are often more sensitive, rapid, easy to perform, cost effective, and are able to be automated and multiplexed.
Various attempts have been made to differentiate Gram-positive and Gram-negative prokaryotes using nucleic acid molecular techniques (see e.g. Bispo, P. J. M., et al., (2011) Ophthalmol. Vis. Sci. 52, 873-881; Klaschik, S. et al. (2002) Journal of Clinical Microbiology 40, 4304-4307; Shigemura, K. et al. (2005) Clin Exp Med 4, 196-201 (2005); Carroll, N. M. et al. (2005) Journal of Clinical Microbiology 38, 1753-1757). These attempts are mostly limited in scope by the number of pathogens they detect and differentiate because of the focus on a limited number of particular pathological specimens (blood, urine, ophthalmic) and pathogen types. The implications of this deficiency could have serious downstream ramifications in, for example, a patient with a prokaryotic infection not able to be detected.
Such reported methods are generally not quantitative, which can be important. Samples rarely consist of a single type of organism and, for pathology samples, are often contaminated with potentially irrelevant and non-pathogenic prokaryotes. In addition, very few solutions or materials or foodstuffs are free of microbes and it is the number of microbes present that determines the level of contamination, lack of quality, or threat to the wellbeing of other organisms. Various methods of quantitation of microbes exist, including but not limited to, plating onto growth media and counting microbial colonies, the use of spectrophotometry to determine turbidity and the use of nanoparticles (Zhao X et al., A rapid bioassay for single bacterial cell quantitation using bioconjugated nanoparticles. PNAS, 101(42): 15027-15032. 2004.)
Few existing methods combine genotypic classification with quantification. One such example is Spiro et al., 2000 (A bead-based method for multiplexed identification and quantitation of DNA sequences using flow cytometry. App Env Micro 66(10): 4258-4265). However, this method does not describe the genotypic classification of microbes. Rather, it demonstrates the ability of bead technology to identify particular DNA sequences in an heterogenous mixture. More recent developments such as Next Generation Sequencing (NGS) hold promise for generating large quantities of data on the microbiome of samples (Claesson, M. J. et al. Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucl Acids Res 38(22), e200 (2010)). However, these technologies currently suffer from a number of limitations especially with respect to determining the simple Gram status of a sample including: lack of sensitivity (samples require PCR amplification prior and in some instances library manufacture prior to sequencing), cost, PCR bias, sequencing inaccuracies, and complex software and algorithms required to interpret the large amount of data generated.
Thus, in the field of microbiology, there is a need for a method of broad microbial classification and quantitation, which is suitably in the form of a single test, that is discriminatory, sensitive, specific, rapid, easy to perform and interpret, inexpensive, lends itself to automation and with the minimum amount of multiplexing, and preferentially performed in the field, remotely, or beside a patient or at least inexpensively in a laboratory. Such a test would allow for rapid and informed management, monitoring, enumeration, quantitation, differentiation, control, eradication, elimination, limitation or removal of such microbes.
In particular, there is a need for a method of broad microbial classification and quantitation of microbes of health significance, such as those that cause sepsis in humans.
Systemic inflammatory response syndrome (SIRS) is an overwhelming whole body reaction that may have an infectious aetiology or non-infectious aetiology (i.e. infection-negative SIRS, or inSIRS). Sepsis is SIRS that occurs during infection. Sepsis in this instance is diagnosed by a clinician (when there is suspected infection) or through culture of an organism. Both SIRS and sepsis are defined by a number of non-specific host response parameters including changes in heart and respiratory rate, body temperature and white cell counts (Levy et al., (2003) Critical Care Medicine 31: 1250-1256; Reinhart et al., (2012) Clinical Microbiology Reviews 25(4): 609-634)
Sepsis and SIRS have had an increasing impact on health care systems worldwide. In the United States from 1993 to 2009, the number of sepsis-related hospital stays more than doubled, increasing by 153% overall, with an average annual increase of 6%. In 2009, 4,600 new patients per day were treated in hospital for sepsis, and nearly one in 23 patients in hospital had sepsis. The in-hospital sepsis mortality rate was approximately 16 percent, which has not changed since 2000, and is more than eight times higher than the mortality rate for other hospital stays. Sepsis was also the most expensive reason for hospitalization in 2009 in the United States, totaling an estimated $15.4 billion in aggregate hospital costs.
SIRS can be triggered by a number of insults including local or systemic infection, trauma, burns, surgery and sterile inflammation and consists of aberrations in at least two of four nominated clinical criteria (temperature, heart rate, respiratory rate and white blood cells). In a comprehensive survey conducted by Rangel-Fausto et al., 68% of admitted patients over a 9-month period met at least two criteria for SIRS and the incidence density of SIRS in the surveyed hospital across different wards was determined to be between 320 and 857 episodes per 1000 patient days (Rangel-Frausto et al. (1995) JAMA 273(2): 117-123. Thus, SIRS has a high incidence in hospitals.
Confirmation of a diagnosis of sepsis usually requires isolation and identification of live pathogens from blood samples using culture, but this technique has its limitations. Microbial culture usually takes a number of days to obtain a positive result and over five days to confirm a negative result. Further, culture has problems with reliability with respect to sensitivity, specificity and predictive value (Jean-Louis Vincent et al., (2006) Critical Care Medicine 34: 344-353; Lamy et al., (2002) Clinical Infectious Diseases 35: 842-850. A large percentage of blood cultures drawn from patients with suspected sepsis are either negative or contaminated (Coburn et al. (2012) JAMA 308, 502-511). Over 90% of all blood cultures drawn from patients are negative. Of the small percentage of blood cultures that are positive (4-7%) up to half are due to contaminants (false positives) as a result of poor sampling technique. False-positive blood cultures can result in an increase in total hospital charges, an increase in median length of hospital stay, and an increase in laboratory charges. Therefore, poor diagnostic procedures for determining the presence of sepsis, including sampling and testing, places a significant financial burden on the healthcare system. Other potential consequences of the diagnostic limitations of blood culture in patients suspected of having sepsis include the use and misuse of broad-spectrum antibiotics, the development of antimicrobial resistance and Clostridium difficile infection, adverse reactions, and increased treatment costs.
Alternative diagnostic approaches to SIRS and sepsis have been extensively investigated and generally fall into one of two categories: pathogen detection, or determination of host response using biomarkers. Promising rapid and sensitive pathogen detection technology includes the use of Polymerase Chain Reaction (PCR), for example, Roche's Lightcycler® SeptiFast, especially when used in conjunction with blood culture (Bauer and Reinhart (2010) International Journal of Medical Microbiology 300: 411-413; Uwe Lodes et al. (2012) Langenbeck's Archives of Surgery 397: 447-455; and Pasqualini et al., (2012) Journal of Clinical Microbiology 50: 1285-1288). A current quandary when using this technology is how best to interpret early positive PCR results in the absence of blood culture results or relevant clinical signs. Such tests are complex and involve multiple multiplexed reactions. Further, accurate quantitation of microorganisms is important in determining the relevance of pathogen detection when using sensitive assay methods. A further technical difficulty associated with PCR-based pathogen detection, especially in peripheral blood samples, is the lack of ability to detect small quantities of pathogen nucleic acid in a background of host nucleic acid.
Given that the majority of patients (>80%) admitted to the tertiary care ICU setting have SIRS, of varying aetiologies including following major surgery, it is of enormous clinical importance that those patients who have a suspected infection or are at high risk of infection can be identified early and be graded and monitored, in order to initiate evidence-based and goal-orientated medical therapy (Kumar, A. et al. (2006) Critical Care Medicine 34, 1589-1596). This is critical, as the acute management plans for SIRS with and without infection are very different. Dependence on empiric treatment means that some patients may be receiving excessive antibiotics while others are receiving treatment (e.g. corticosteroids) that is immunosuppressive because a clear site of infection has not been identified. Thus, there is a continued need for a test that is able to differentiate infection-negative SIRS (inSIRS) from infection-positive-SIRS (e.g. sepsis), quantitate microbial DNA, differentiate prokaryotic and eukaryotic DNA and differentiate Gram-positive from Gram-negative DNA across a broad range of potential pathogens, so as to assist clinicians in making appropriate patient management and treatment decisions in such patients.
There is also a need for rapid assays and tests that further classify bacteria beyond Gram-positive and Gram-negative, so as to assist the clinician to determine an appropriate course of treatment. Traditional methods typically require culturing the bacteria, typically for 2 to 10 days, depending on the species of bacteria (e.g. slow-growing and fastidious organisms such as mycobacteria can take 10 days). This culturing of bacteria, in particular anaerobic bacteria, can be labour intensive, burdensome and require special equipment and reagents. Once cultured, the first procedure the laboratory generally performs is a Gram stain and morphology assessment, the results of which are reported to clinicians promptly. A Gram stain allows for classification of the bacteria grown, if grown, into the following groups: Gram-positive cocci, Gram-positive bacilli, Gram-negative bacilli, Gram-negative cocci, anaerobes, and Candida spp. Such information, in combination with other data, may be sufficient to make an appropriate antibiotic choice. Further tests can then help determine antibiotic sensitivity and species identification, although these tests are not always performed due to cost and logistics.
Because of the high mortality associated with bacteraemia, the dangers of under treating some infections, or concern about using inappropriate antibiotics, clinicians tend to order blood cultures liberally and put patients on empirical antibiotics soon after blood cultures are taken. Thus, patients with suspected sepsis are generally put on empirical antibiotics shortly after blood cultures are taken and prior to receiving any culture or further test results. The choice of empirical antibiotic used depends on factors such as: site of infection (e.g. respiratory, skin, urinary, gastrointestinal etc), whether the infection is hospital acquired or community acquired, epidemiology of pathogens, hospital microbial resistance patterns, whether the patient has been or is on antibiotics, patient allergies, and patient co-morbidities and known antibiotic toxicities. Guidance to clinicians on choice of empirical antibiotics is often available through hospital publications, health maintenance organisations (HMOs), specialist organisations and the scientific literature (by example see http://www.clevelandclinicmeded.com/medicalpubs/antimicrobial-guidelines/; http://www.uphs.upenn.edu/bugdrug/; Huttner B, Jones M, Huttner A, Rubin M, Samore M H (2013) Antibiotic prescription practices for pneumonia, skin and soft tissue infections and urinary tract infections throughout the US Veterans Affairs system. J Antimicrob Chemother 68: 2393-2399. doi:10.1093/jac/dkt171; Snydman D R (2012) Empiric antibiotic selection strategies for healthcare-associated pneumonia, intra-abdominal infections, and catheter-associated bacteremia. J Hosp Med 7 Suppl 1: S2S12. doi:10.1002/jhm.980; Maxwell D J, Easton K L, Brien J-A E, Kaye K I (2005) Antibiotic guidelines in NSW hospitals. Aust Health Rev 29: 416-421.). However, because of the development of microbial antibiotic resistance, the aim and purpose of microbiology testing is to provide evidence and guidance on the appropriate use of narrow spectrum antibiotics and subsequently the reduction in the use of empirical broad-spectrum antibiotics. Therefore, a test that can provide timely guidance on the appropriate use of narrow spectrum antibiotics is needed, preferably without having to grow the organism.